The authors measured root morphological and architectural traits of 22 different dominant plant species across 16 Inner Mongolia grassland sites along soil water gradients, and analyzed the response of these root traits (diameter, length, SRL, RTD, BrIntensity and BrRatio) to four environmental factors (MAT, MAP, Soil N and Soil C). The results showed that variation of absorptive root diameter, tissue density and specific root length among different species was 7, 9, and 15 times, respectively. There was a significant positive correlation between root diameter and lateral root length, but negative correlation between root diameter and root branching intensity. Responses of both absorptive and non-absorptive roots to precipitation and soil nitrogen were species-specific. When using different combinations of root traits to describe plant adaptation strategies, different species’ root traits respond to environmental changes with different degrees and direction of variation, resulting in a diversity of plant adaptation strategies.

In response to the inadequacies of current graph compression methods, such as higher time complexity, dependence on experiences to set parameters, too many parameters to adjust, compression loss, ignoring the community structure of network, a social network compression method is proposed based on the importance of the community nodes. The method include community discovery algorithm (GS) based on greedy strategy and social network compression algorithm (SNC). Adopting topological potential theory GS algorithm is not only capable of discovering communities but also capable of mining important nodes in the communities. SNC algorithm takes communities as targets, achieves lossless compression while maintaining the connections between communities, and keeps important nodes in communities or basic community structure if necessary. The feasibility and effectiveness of the method are verified in experiments.